Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data
arXiv:2011.10243 · doi:10.1093/mnras/stab1461
Abstract
The point spread function (PSF) reflects states of a telescope and plays an important role in development of data processing methods, such as PSF based astrometry, photometry and image restoration. However, for wide field small aperture telescopes (WFSATs), estimating PSF in any position of the whole field of view is hard, because aberrations induced by the optical system are quite complex and the signal to noise ratio of star images is often too low for PSF estimation. In this paper, we further develop our deep neural network (DNN) based PSF modelling method and show its applications in PSF estimation. During the telescope alignment and testing stage, our method collects system calibration data through modification of optical elements within engineering tolerances (tilting and decentering). Then we use these data to train a DNN (Tel--Net). After training, the Tel--Net can estimate PSF in any field of view from several discretely sampled star images. We use both simulated and experimental data to test performance of our method. The results show that the Tel--Net can successfully reconstruct PSFs of WFSATs of any states and in any positions of the FoV. Its results are significantly more precise than results obtained by the compared classic method - Inverse Distance Weight (IDW) interpolation. Our method provides foundations for developing of deep neural network based data processing methods for WFSATs, which require strong prior information of PSFs.
arXiv commentAccepted by the MNRAS
References in corpus (24)
- Adam: A Method for Stochastic Optimization
- SparsePak: A Formatted Fiber Field Unit for The WIYN Telescope Bench Spectrograph. I. Design, Construction, and Calibration
- Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
- Principal Component Analysis of the Time- and Position-Dependent Point Spread Function of the Advanced Camera for Surveys
- Fixup Initialization: Residual Learning Without Normalization
- Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning
- Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data
- The BRITE Constellation nanosatellite mission: Testing, commissioning and operations
- Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes
- Physics-based model of the adaptive-optics corrected point-spread-function
- DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning
- Reconstruction of the ground-layer adaptive-optics point spread function for MUSE Wide Field Mode observations
- Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
- Building the Evryscope: Hardware Design and Performance
- PSF--NET: A Non-parametric Point Spread Function Model for Ground Based Optical Telescopes
- Evryscope and K2 Constraints on TRAPPIST-1 Superflare Occurrence and Planetary Habitability
- Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks
- Vetting the optical transient candidates detected by the GWAC network using convolutional neural networks
- Optical Transient Object Classification in Wide Field Small Aperture Telescopes with Neural Networks
- Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder
- Solar Image Restoration with the Cycle-GAN Based on Multi-Fractal Properties of Texture Features
- PRIME: Psf Reconstruction and Identification for Multiple sources characterization Enhancement. Application to Keck NIRC2 imager
- A method for space-variant deblurring with application to adaptive optics imaging in astronomy
- Data--driven Image Restoration with Option--driven Learning for Big and Small Astronomical Image Datasets